Overview

Dataset statistics

Number of variables12
Number of observations13903
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory105.0 B

Variable types

DateTime1
Numeric10
Categorical1

Alerts

ignore has constant value ""Constant
open is highly overall correlated with high and 7 other fieldsHigh correlation
high is highly overall correlated with open and 7 other fieldsHigh correlation
low is highly overall correlated with open and 7 other fieldsHigh correlation
close is highly overall correlated with open and 7 other fieldsHigh correlation
volume is highly overall correlated with open and 4 other fieldsHigh correlation
quote_asset_volume is highly overall correlated with open and 5 other fieldsHigh correlation
number_of_trades is highly overall correlated with open and 5 other fieldsHigh correlation
taker_buy_base_asset_volume is highly overall correlated with open and 4 other fieldsHigh correlation
taker_buy_quote_asset_volume is highly overall correlated with open and 5 other fieldsHigh correlation
volume is highly skewed (γ1 = 31.46906051)Skewed
taker_buy_base_asset_volume is highly skewed (γ1 = 31.67124314)Skewed
volume has unique valuesUnique
quote_asset_volume has unique valuesUnique
taker_buy_base_asset_volume has unique valuesUnique
taker_buy_quote_asset_volume has unique valuesUnique

Reproduction

Analysis started2023-08-16 02:55:37.493871
Analysis finished2023-08-16 02:56:01.756558
Duration24.26 seconds
Software versionydata-profiling vv4.5.0
Download configurationconfig.json

Variables

timestamp
Date

Distinct2052
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Memory size230.3 KiB
Minimum2018-01-01 00:00:00
Maximum2023-08-14 00:00:00
2023-08-15T23:56:01.882554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:56:02.133547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

open
Real number (ℝ)

HIGH CORRELATION 

Distinct13192
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3310.9469
Minimum0.0014971
Maximum67525.82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size230.3 KiB
2023-08-15T23:56:02.376554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0014971
5-th percentile0.037744
Q10.293805
median27.1106
Q3416.815
95-th percentile24284.345
Maximum67525.82
Range67525.819
Interquartile range (IQR)416.52119

Descriptive statistics

Standard deviation9640.5859
Coefficient of variation (CV)2.9117307
Kurtosis15.090699
Mean3310.9469
Median Absolute Deviation (MAD)27.06814
Skewness3.8165071
Sum46032095
Variance92940897
MonotonicityNot monotonic
2023-08-15T23:56:02.626531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
317.1 4
 
< 0.1%
247.8 4
 
< 0.1%
246.2 4
 
< 0.1%
240.3 4
 
< 0.1%
0.07051 3
 
< 0.1%
0.4044 3
 
< 0.1%
240.7 3
 
< 0.1%
0.2993 3
 
< 0.1%
238.6 3
 
< 0.1%
0.0429 3
 
< 0.1%
Other values (13182) 13869
99.8%
ValueCountFrequency (%)
0.0014971 1
< 0.1%
0.0015927 1
< 0.1%
0.0016002 1
< 0.1%
0.0016219 1
< 0.1%
0.0016603 1
< 0.1%
0.0016903 1
< 0.1%
0.0017202 1
< 0.1%
0.0017222 1
< 0.1%
0.0017226 1
< 0.1%
0.00176 1
< 0.1%
ValueCountFrequency (%)
67525.82 1
< 0.1%
66947.67 1
< 0.1%
66001.4 1
< 0.1%
65519.11 1
< 0.1%
64882.42 1
< 0.1%
64774.25 1
< 0.1%
64380.01 1
< 0.1%
64280.59 1
< 0.1%
64122.22 1
< 0.1%
63606.73 1
< 0.1%

high
Real number (ℝ)

HIGH CORRELATION 

Distinct12803
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3398.8724
Minimum0.0016428
Maximum69000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size230.3 KiB
2023-08-15T23:56:03.029519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0016428
5-th percentile0.0395
Q10.303835
median28.1797
Q3429.5
95-th percentile25010.853
Maximum69000
Range68999.998
Interquartile range (IQR)429.19617

Descriptive statistics

Standard deviation9896.679
Coefficient of variation (CV)2.9117536
Kurtosis15.072845
Mean3398.8724
Median Absolute Deviation (MAD)28.13578
Skewness3.816884
Sum47254523
Variance97944255
MonotonicityNot monotonic
2023-08-15T23:56:03.284511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
245 6
 
< 0.1%
247 6
 
< 0.1%
292 5
 
< 0.1%
205 5
 
< 0.1%
248 4
 
< 0.1%
185 4
 
< 0.1%
0.135 4
 
< 0.1%
426 4
 
< 0.1%
244.9 4
 
< 0.1%
0.05 4
 
< 0.1%
Other values (12793) 13857
99.7%
ValueCountFrequency (%)
0.0016428 1
< 0.1%
0.0016873 1
< 0.1%
0.0017 1
< 0.1%
0.0017771 1
< 0.1%
0.0017957 1
< 0.1%
0.0018001 1
< 0.1%
0.001809 1
< 0.1%
0.0018286 1
< 0.1%
0.0018329 1
< 0.1%
0.0018395 1
< 0.1%
ValueCountFrequency (%)
69000 1
< 0.1%
68524.25 1
< 0.1%
67789 1
< 0.1%
67000 1
< 0.1%
66639.74 1
< 0.1%
66401.82 1
< 0.1%
65600.07 1
< 0.1%
65550.51 1
< 0.1%
65450.7 1
< 0.1%
65000 1
< 0.1%

low
Real number (ℝ)

HIGH CORRELATION 

Distinct12744
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3212.1957
Minimum0.0011345
Maximum66222.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size230.3 KiB
2023-08-15T23:56:03.614501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0011345
5-th percentile0.03624
Q10.28175
median25.9
Q3403.245
95-th percentile23763.02
Maximum66222.4
Range66222.399
Interquartile range (IQR)402.96325

Descriptive statistics

Standard deviation9350.2875
Coefficient of variation (CV)2.9108711
Kurtosis15.063509
Mean3212.1957
Median Absolute Deviation (MAD)25.85873
Skewness3.8115795
Sum44659156
Variance87427877
MonotonicityNot monotonic
2023-08-15T23:56:03.892493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 6
 
< 0.1%
0.12 5
 
< 0.1%
242 5
 
< 0.1%
0.28 5
 
< 0.1%
0.235 5
 
< 0.1%
0.082 5
 
< 0.1%
9.81 4
 
< 0.1%
0.0024 4
 
< 0.1%
0.3486 4
 
< 0.1%
273.2 4
 
< 0.1%
Other values (12734) 13856
99.7%
ValueCountFrequency (%)
0.0011345 1
< 0.1%
0.0013478 1
< 0.1%
0.0014003 1
< 0.1%
0.0015442 1
< 0.1%
0.001546 1
< 0.1%
0.001565 1
< 0.1%
0.0016295 1
< 0.1%
0.001633 1
< 0.1%
0.0016412 1
< 0.1%
0.0016843 1
< 0.1%
ValueCountFrequency (%)
66222.4 1
< 0.1%
64100 1
< 0.1%
63576.27 1
< 0.1%
63481.4 1
< 0.1%
63400 1
< 0.1%
63360.22 1
< 0.1%
63273.58 1
< 0.1%
62822.9 1
< 0.1%
62278 1
< 0.1%
62020 1
< 0.1%

close
Real number (ℝ)

HIGH CORRELATION 

Distinct13179
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3312.1872
Minimum0.0015817
Maximum67525.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size230.3 KiB
2023-08-15T23:56:04.152494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0015817
5-th percentile0.037932
Q10.29394
median27.1181
Q3416.745
95-th percentile24303.282
Maximum67525.83
Range67525.828
Interquartile range (IQR)416.45106

Descriptive statistics

Standard deviation9642.7374
Coefficient of variation (CV)2.91129
Kurtosis15.076802
Mean3312.1872
Median Absolute Deviation (MAD)27.07567
Skewness3.8149714
Sum46049338
Variance92982386
MonotonicityNot monotonic
2023-08-15T23:56:04.549473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0836 4
 
< 0.1%
244.5 4
 
< 0.1%
290.4 4
 
< 0.1%
0.3126 4
 
< 0.1%
44.98 4
 
< 0.1%
244.3 3
 
< 0.1%
13.71 3
 
< 0.1%
0.4662 3
 
< 0.1%
148.1 3
 
< 0.1%
0.06845 3
 
< 0.1%
Other values (13169) 13868
99.7%
ValueCountFrequency (%)
0.0015817 1
< 0.1%
0.0015996 1
< 0.1%
0.0016002 1
< 0.1%
0.0016219 1
< 0.1%
0.0016632 1
< 0.1%
0.0016932 1
< 0.1%
0.0017099 1
< 0.1%
0.0017264 1
< 0.1%
0.001746 1
< 0.1%
0.00177 1
< 0.1%
ValueCountFrequency (%)
67525.83 1
< 0.1%
66947.66 1
< 0.1%
66001.41 1
< 0.1%
65519.1 1
< 0.1%
64882.43 1
< 0.1%
64774.26 1
< 0.1%
64380 1
< 0.1%
64280.59 1
< 0.1%
64122.23 1
< 0.1%
63606.74 1
< 0.1%

volume
Real number (ℝ)

HIGH CORRELATION  SKEWED  UNIQUE 

Distinct13903
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4455328 Ă— 108
Minimum30.208
Maximum1.0907369 Ă— 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size230.3 KiB
2023-08-15T23:56:04.847463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30.208
5-th percentile3602.3168
Q1148748.12
median973857.75
Q315834297
95-th percentile9.1839877 Ă— 108
Maximum1.0907369 Ă— 1011
Range1.0907369 Ă— 1011
Interquartile range (IQR)15685549

Descriptive statistics

Standard deviation1.9746319 Ă— 109
Coefficient of variation (CV)8.0744446
Kurtosis1388.1154
Mean2.4455328 Ă— 108
Median Absolute Deviation (MAD)948428.89
Skewness31.469061
Sum3.4000242 Ă— 1012
Variance3.8991712 Ă— 1018
MonotonicityNot monotonic
2023-08-15T23:56:05.152454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8609.915844 1
 
< 0.1%
1155338.3 1
 
< 0.1%
1034314.7 1
 
< 0.1%
3867340.7 1
 
< 0.1%
1751137.1 1
 
< 0.1%
1532868 1
 
< 0.1%
2297658.9 1
 
< 0.1%
2331073.1 1
 
< 0.1%
1677351.6 1
 
< 0.1%
1595838.8 1
 
< 0.1%
Other values (13893) 13893
99.9%
ValueCountFrequency (%)
30.208 1
< 0.1%
138.3421 1
< 0.1%
175.905 1
< 0.1%
229.57441 1
< 0.1%
254.93685 1
< 0.1%
266.78477 1
< 0.1%
283.11115 1
< 0.1%
285.42792 1
< 0.1%
290.3541 1
< 0.1%
296.43034 1
< 0.1%
ValueCountFrequency (%)
1.090736932 Ă— 10111
< 0.1%
1.029897951 Ă— 10111
< 0.1%
5.751054536 Ă— 10101
< 0.1%
5.455938857 Ă— 10101
< 0.1%
4.879564252 Ă— 10101
< 0.1%
4.633334726 Ă— 10101
< 0.1%
4.457779773 Ă— 10101
< 0.1%
3.88378463 Ă— 10101
< 0.1%
3.668098972 Ă— 10101
< 0.1%
2.964880892 Ă— 10101
< 0.1%

close_time
Real number (ℝ)

Distinct2057
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6091968 Ă— 1012
Minimum1.5148512 Ă— 1012
Maximum1.6920576 Ă— 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size230.3 KiB
2023-08-15T23:56:05.457444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5148512 Ă— 1012
5-th percentile1.5321312 Ă— 1012
Q11.570968 Ă— 1012
median1.6093728 Ă— 1012
Q31.6479072 Ă— 1012
95-th percentile1.6844544 Ă— 1012
Maximum1.6920576 Ă— 1012
Range1.772064 Ă— 1011
Interquartile range (IQR)7.69392 Ă— 1010

Descriptive statistics

Standard deviation4.7030976 Ă— 1010
Coefficient of variation (CV)0.029226368
Kurtosis-1.0309898
Mean1.6091968 Ă— 1012
Median Absolute Deviation (MAD)3.8448 Ă— 1010
Skewness-0.032238616
Sum2.2372663 Ă— 1016
Variance2.2119127 Ă— 1021
MonotonicityNot monotonic
2023-08-15T23:56:05.714436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.6036704 Ă— 10128
 
0.1%
1.6003008 Ă— 10128
 
0.1%
1.600992 Ă— 10128
 
0.1%
1.6009056 Ă— 10128
 
0.1%
1.6008192 Ă— 10128
 
0.1%
1.6007328 Ă— 10128
 
0.1%
1.6006464 Ă— 10128
 
0.1%
1.60056 Ă— 10128
 
0.1%
1.6004736 Ă— 10128
 
0.1%
1.6003872 Ă— 10128
 
0.1%
Other values (2047) 13823
99.4%
ValueCountFrequency (%)
1.5148512 Ă— 10123
< 0.1%
1.5149376 Ă— 10123
< 0.1%
1.515024 Ă— 10123
< 0.1%
1.5151104 Ă— 10123
< 0.1%
1.5151968 Ă— 10123
< 0.1%
1.5152832 Ă— 10123
< 0.1%
1.5153696 Ă— 10123
< 0.1%
1.515456 Ă— 10123
< 0.1%
1.5155424 Ă— 10123
< 0.1%
1.5156288 Ă— 10123
< 0.1%
ValueCountFrequency (%)
1.6920576 Ă— 10128
0.1%
1.6919712 Ă— 10128
0.1%
1.6918848 Ă— 10128
0.1%
1.6917984 Ă— 10128
0.1%
1.691712 Ă— 10128
0.1%
1.6916256 Ă— 10128
0.1%
1.6915392 Ă— 10128
0.1%
1.6914528 Ă— 10128
0.1%
1.6913664 Ă— 10128
0.1%
1.69128 Ă— 10128
0.1%

quote_asset_volume
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct13903
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.729724 Ă— 108
Minimum1364.507
Maximum1.7598562 Ă— 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size230.3 KiB
2023-08-15T23:56:05.968443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1364.507
5-th percentile72625.635
Q11650088.7
median62576189
Q33.3785556 Ă— 108
95-th percentile2.4754624 Ă— 109
Maximum1.7598562 Ă— 1010
Range1.759856 Ă— 1010
Interquartile range (IQR)3.3620547 Ă— 108

Descriptive statistics

Standard deviation1.1477513 Ă— 109
Coefficient of variation (CV)2.4266772
Kurtosis38.66932
Mean4.729724 Ă— 108
Median Absolute Deviation (MAD)62396832
Skewness5.1195865
Sum6.5757353 Ă— 1012
Variance1.3173331 Ă— 1018
MonotonicityNot monotonic
2023-08-15T23:56:06.300418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
114799747.4 1
 
< 0.1%
379218.5199 1
 
< 0.1%
366245.9354 1
 
< 0.1%
1430689.347 1
 
< 0.1%
630906.3538 1
 
< 0.1%
541676.0518 1
 
< 0.1%
819621.0482 1
 
< 0.1%
842090.4445 1
 
< 0.1%
612038.1226 1
 
< 0.1%
579719.9926 1
 
< 0.1%
Other values (13893) 13893
99.9%
ValueCountFrequency (%)
1364.506957 1
< 0.1%
1439.04689 1
< 0.1%
1583.539436 1
< 0.1%
1632.474886 1
< 0.1%
1763.6767 1
< 0.1%
1801.183385 1
< 0.1%
1937.348977 1
< 0.1%
1964.217308 1
< 0.1%
2301.256112 1
< 0.1%
2367.736699 1
< 0.1%
ValueCountFrequency (%)
1.759856186 Ă— 10101
< 0.1%
1.74653071 Ă— 10101
< 0.1%
1.647668107 Ă— 10101
< 0.1%
1.582499608 Ă— 10101
< 0.1%
1.475838431 Ă— 10101
< 0.1%
1.429100574 Ă— 10101
< 0.1%
1.370074615 Ă— 10101
< 0.1%
1.347769493 Ă— 10101
< 0.1%
1.330042454 Ă— 10101
< 0.1%
1.27377197 Ă— 10101
< 0.1%

number_of_trades
Real number (ℝ)

HIGH CORRELATION 

Distinct12120
Distinct (%)87.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean459063.37
Minimum4
Maximum15330777
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size230.3 KiB
2023-08-15T23:56:06.636408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile207
Q15241
median129247
Q3443478
95-th percentile1785232.4
Maximum15330777
Range15330773
Interquartile range (IQR)438237

Descriptive statistics

Standard deviation1047767
Coefficient of variation (CV)2.2824016
Kurtosis46.647728
Mean459063.37
Median Absolute Deviation (MAD)128498
Skewness5.8385057
Sum6.382358 Ă— 109
Variance1.0978156 Ă— 1012
MonotonicityNot monotonic
2023-08-15T23:56:07.034395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66 13
 
0.1%
55 11
 
0.1%
77 9
 
0.1%
75 9
 
0.1%
282 9
 
0.1%
194 9
 
0.1%
377 8
 
0.1%
284 8
 
0.1%
243 8
 
0.1%
655 8
 
0.1%
Other values (12110) 13811
99.3%
ValueCountFrequency (%)
4 1
 
< 0.1%
12 1
 
< 0.1%
13 1
 
< 0.1%
17 1
 
< 0.1%
18 1
 
< 0.1%
19 1
 
< 0.1%
21 1
 
< 0.1%
22 2
< 0.1%
23 3
< 0.1%
24 4
< 0.1%
ValueCountFrequency (%)
15330777 1
< 0.1%
15223589 1
< 0.1%
14820760 1
< 0.1%
14530601 1
< 0.1%
13605474 1
< 0.1%
13113359 1
< 0.1%
12719887 1
< 0.1%
12613034 1
< 0.1%
12106261 1
< 0.1%
11885014 1
< 0.1%

taker_buy_base_asset_volume
Real number (ℝ)

HIGH CORRELATION  SKEWED  UNIQUE 

Distinct13903
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2163826 Ă— 108
Minimum30.068
Maximum5.419393 Ă— 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size230.3 KiB
2023-08-15T23:56:07.377386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30.068
5-th percentile1706.3379
Q172657.92
median477028.87
Q38041185.1
95-th percentile4.5399713 Ă— 108
Maximum5.419393 Ă— 1010
Range5.419393 Ă— 1010
Interquartile range (IQR)7968527.1

Descriptive statistics

Standard deviation9.8897795 Ă— 108
Coefficient of variation (CV)8.1304843
Kurtosis1401.9053
Mean1.2163826 Ă— 108
Median Absolute Deviation (MAD)464315.68
Skewness31.671243
Sum1.6911367 Ă— 1012
Variance9.7807738 Ă— 1017
MonotonicityNot monotonic
2023-08-15T23:56:07.651377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3961.938946 1
 
< 0.1%
609355.2 1
 
< 0.1%
554677.6 1
 
< 0.1%
1695824.5 1
 
< 0.1%
560013.9 1
 
< 0.1%
537692.3 1
 
< 0.1%
1132652.2 1
 
< 0.1%
1393904.7 1
 
< 0.1%
702840.6 1
 
< 0.1%
734217.3 1
 
< 0.1%
Other values (13893) 13893
99.9%
ValueCountFrequency (%)
30.068 1
< 0.1%
61.22485 1
< 0.1%
65.75459 1
< 0.1%
71.46 1
< 0.1%
74.4858 1
< 0.1%
75.12533 1
< 0.1%
99.72737 1
< 0.1%
102.10344 1
< 0.1%
103.27294 1
< 0.1%
103.617 1
< 0.1%
ValueCountFrequency (%)
5.419393024 Ă— 10101
< 0.1%
5.236644697 Ă— 10101
< 0.1%
2.942236996 Ă— 10101
< 0.1%
2.675672206 Ă— 10101
< 0.1%
2.476810586 Ă— 10101
< 0.1%
2.307243258 Ă— 10101
< 0.1%
2.217160585 Ă— 10101
< 0.1%
1.98756494 Ă— 10101
< 0.1%
1.825278002 Ă— 10101
< 0.1%
1.461480517 Ă— 10101
< 0.1%

taker_buy_quote_asset_volume
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct13903
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3518263 Ă— 108
Minimum159.20869
Maximum9.0139691 Ă— 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size230.3 KiB
2023-08-15T23:56:07.890719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum159.20869
5-th percentile30401.883
Q1807559.99
median31110941
Q31.67947 Ă— 108
95-th percentile1.2333995 Ă— 109
Maximum9.0139691 Ă— 109
Range9.0139689 Ă— 109
Interquartile range (IQR)1.6713944 Ă— 108

Descriptive statistics

Standard deviation5.7047871 Ă— 108
Coefficient of variation (CV)2.4256838
Kurtosis39.588393
Mean2.3518263 Ă— 108
Median Absolute Deviation (MAD)31035458
Skewness5.1585899
Sum3.2697442 Ă— 1012
Variance3.2544596 Ă— 1017
MonotonicityNot monotonic
2023-08-15T23:56:08.133719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52809747.44 1
 
< 0.1%
200046.0177 1
 
< 0.1%
196679.8594 1
 
< 0.1%
624913.0839 1
 
< 0.1%
201712.5501 1
 
< 0.1%
190535.4904 1
 
< 0.1%
404311.5378 1
 
< 0.1%
502489.4079 1
 
< 0.1%
257527.1946 1
 
< 0.1%
267323.0486 1
 
< 0.1%
Other values (13893) 13893
99.9%
ValueCountFrequency (%)
159.208694 1
< 0.1%
229.248615 1
< 0.1%
255.400341 1
< 0.1%
316.991198 1
< 0.1%
582.694168 1
< 0.1%
591.952679 1
< 0.1%
627.613604 1
< 0.1%
638.874535 1
< 0.1%
693.209687 1
< 0.1%
702.733383 1
< 0.1%
ValueCountFrequency (%)
9013969075 1
< 0.1%
8783916248 1
< 0.1%
8392477832 1
< 0.1%
7976620082 1
< 0.1%
7271200924 1
< 0.1%
7157472157 1
< 0.1%
6724156565 1
< 0.1%
6680704888 1
< 0.1%
6410032517 1
< 0.1%
6245795115 1
< 0.1%

ignore
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size230.3 KiB
0
13903 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13903
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13903
100.0%

Length

2023-08-15T23:56:08.417697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-15T23:56:08.629690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 13903
100.0%

Most occurring characters

ValueCountFrequency (%)
0 13903
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13903
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13903
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13903
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13903
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13903
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13903
100.0%

Interactions

2023-08-15T23:55:59.024592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:39.453414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:41.487723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:44.054875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:45.759821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:47.569407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:49.826338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:52.844245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:54.746187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:56.765661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:59.177638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:39.701406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:42.491692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:44.216869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:45.944815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:47.747409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:50.209326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:53.021239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:54.955181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:56.962656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:59.337633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:39.864415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:42.709686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:44.412863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:46.147809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:47.907396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:50.648313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:53.189234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:55.166174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:57.150649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:59.496628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:40.034396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:42.894538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:44.581867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:46.301804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:48.085392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:50.941303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:53.350229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:55.360168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:57.314644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:59.651632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:40.249462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:43.062528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:44.752852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:46.479799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:48.338383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:51.181296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:53.522239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:55.560125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:57.491638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:59.806633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:40.443442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:43.217535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:44.912864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:46.641795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:48.540377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:51.385290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:53.694234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:55.743104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:57.685632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:59.967613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:40.632453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:43.394521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:45.077858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:46.818803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:48.796369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:51.607282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:53.955211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:56.025094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:58.127619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:56:00.129608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:40.891742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:43.559507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:45.265836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:47.024437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:49.064362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:51.823277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:54.138206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:56.229089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:58.400611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:56:00.290619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:41.082735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:43.735884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:45.458830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:47.198422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:49.316354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:52.027270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:54.365198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:56.431082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:58.643604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:56:00.436599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:41.316728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:43.901879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:45.612826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:47.383413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:49.506347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:52.209265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:54.541208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:56.594667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-15T23:55:58.846597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-08-15T23:56:08.795686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
openhighlowclosevolumeclose_timequote_asset_volumenumber_of_tradestaker_buy_base_asset_volumetaker_buy_quote_asset_volume
open1.0001.0001.0001.000-0.6850.1310.6620.619-0.6740.663
high1.0001.0001.0001.000-0.6830.1300.6640.621-0.6720.666
low1.0001.0001.0001.000-0.6860.1310.6610.617-0.6750.662
close1.0001.0001.0001.000-0.6840.1310.6630.619-0.6730.664
volume-0.685-0.683-0.686-0.6841.0000.1140.0380.0870.9980.035
close_time0.1310.1300.1310.1310.1141.0000.3230.3420.1170.320
quote_asset_volume0.6620.6640.6610.6630.0380.3231.0000.9900.0490.999
number_of_trades0.6190.6210.6170.6190.0870.3420.9901.0000.0970.989
taker_buy_base_asset_volume-0.674-0.672-0.675-0.6730.9980.1170.0490.0971.0000.049
taker_buy_quote_asset_volume0.6630.6660.6620.6640.0350.3200.9990.9890.0491.000

Missing values

2023-08-15T23:56:00.690590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-15T23:56:01.542564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

timestampopenhighlowclosevolumeclose_timequote_asset_volumenumber_of_tradestaker_buy_base_asset_volumetaker_buy_quote_asset_volumeignore
BTCUSDT02018-01-0113715.6513818.5512750.0013380.008609.91584415148511999991.147997e+08105595.03961.9389465.280975e+070
12018-01-0213382.1615473.4912890.0214675.1120078.09211115149375999992.797171e+08177728.011346.3267391.580801e+080
22018-01-0314690.0015307.5614150.0014919.5115905.66763915150239999992.361169e+08162787.08994.9535661.335873e+080
32018-01-0414919.5115280.0013918.0415059.5421329.64957415151103999993.127816e+08170310.012680.8129511.861168e+080
42018-01-0515059.5617176.2414600.0016960.3923251.49112515151967999993.693220e+08192969.013346.6222932.118299e+080
52018-01-0616960.3917143.1316011.2117069.7918571.45750815152831999993.092169e+08158242.011007.1640561.834178e+080
62018-01-0717069.7917099.9615610.0016150.0312493.12555815153695999992.061947e+08120269.06779.3205081.121512e+080
72018-01-0816218.8516322.3012812.0014902.5426600.60991215154559999993.965700e+08208642.013756.8443892.060496e+080
82018-01-0914902.5415500.0014011.0514400.0014315.00425315155423999992.106302e+08156656.06841.2332871.006986e+080
92018-01-1014401.0014955.6613131.3114907.0917411.00165515156287999992.440114e+08161476.08529.5546751.195701e+080
timestampopenhighlowclosevolumeclose_timequote_asset_volumenumber_of_tradestaker_buy_base_asset_volumetaker_buy_quote_asset_volumeignore
DOGEUSDT14922023-08-050.073460.077080.072920.075711.062980e+0916912799999998.036190e+07156317.0545409723.04.124821e+070
14932023-08-060.075720.076490.074070.074276.672252e+0816913663999995.016737e+07101876.0326758910.02.457553e+070
14942023-08-070.074270.075730.071700.073546.860242e+0816914527999995.066166e+07113898.0310617392.02.296354e+070
14952023-08-080.073540.075480.073210.074965.735745e+0816915391999994.264991e+0791886.0274952081.02.045537e+070
14962023-08-090.074970.076220.074190.075414.948366e+0816916255999993.722041e+0780476.0234370475.01.762911e+070
14972023-08-100.075420.076950.075140.075886.145324e+0816917119999994.664747e+07101988.0315622223.02.396316e+070
14982023-08-110.075870.076110.074840.075743.037878e+0816917983999992.295508e+0751419.0139078876.01.051290e+070
14992023-08-120.075740.077450.075610.076705.880606e+0816918847999994.505888e+0784459.0316164628.02.423127e+070
15002023-08-130.076700.076890.074300.074794.685280e+0816919711999993.548866e+0771384.0212851900.01.612911e+070
15012023-08-140.074790.077110.073650.074697.796326e+0816920575999995.847326e+07121040.0340949124.02.559942e+070